/*
 * Copyright (c) 2012, 2013, Oracle and/or its affiliates. All rights reserved.
 * ORACLE PROPRIETARY/CONFIDENTIAL. Use is subject to license terms.
 *
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 */
package java.util.stream;

import java.util.AbstractMap;
import java.util.AbstractSet;
import java.util.ArrayList;
import java.util.Arrays;
import java.util.Collection;
import java.util.Collections;
import java.util.Comparator;
import java.util.DoubleSummaryStatistics;
import java.util.EnumSet;
import java.util.HashMap;
import java.util.HashSet;
import java.util.IntSummaryStatistics;
import java.util.Iterator;
import java.util.List;
import java.util.LongSummaryStatistics;
import java.util.Map;
import java.util.Objects;
import java.util.Optional;
import java.util.Set;
import java.util.StringJoiner;
import java.util.concurrent.ConcurrentHashMap;
import java.util.concurrent.ConcurrentMap;
import java.util.function.BiConsumer;
import java.util.function.BiFunction;
import java.util.function.BinaryOperator;
import java.util.function.Consumer;
import java.util.function.Function;
import java.util.function.Predicate;
import java.util.function.Supplier;
import java.util.function.ToDoubleFunction;
import java.util.function.ToIntFunction;
import java.util.function.ToLongFunction;

/**
 * Implementations of {@link Collector} that implement various useful reduction operations, such as
 * accumulating elements into collections, summarizing elements according to various criteria, etc.
 *
 * <p>The following are examples of using the predefined collectors to perform common mutable
 * reduction tasks:
 *
 * <pre>{@code
 *     // Accumulate names into a List
 *     List<String> list = people.stream().map(Person::getName).collect(Collectors.toList());
 *
 *     // Accumulate names into a TreeSet
 *     Set<String> set = people.stream().map(Person::getName).collect(Collectors.toCollection(TreeSet::new));
 *
 *     // Convert elements to strings and concatenate them, separated by commas
 *     String joined = things.stream()
 *                           .map(Object::toString)
 *                           .collect(Collectors.joining(", "));
 *
 *     // Compute sum of salaries of employee
 *     int total = employees.stream()
 *                          .collect(Collectors.summingInt(Employee::getSalary)));
 *
 *     // Group employees by department
 *     Map<Department, List<Employee>> byDept
 *         = employees.stream()
 *                    .collect(Collectors.groupingBy(Employee::getDepartment));
 *
 *     // Compute sum of salaries by department
 *     Map<Department, Integer> totalByDept
 *         = employees.stream()
 *                    .collect(Collectors.groupingBy(Employee::getDepartment,
 *                                                   Collectors.summingInt(Employee::getSalary)));
 *
 *     // Partition students into passing and failing
 *     Map<Boolean, List<Student>> passingFailing =
 *         students.stream()
 *                 .collect(Collectors.partitioningBy(s -> s.getGrade() >= PASS_THRESHOLD));
 *
 * }</pre>
 *
 * @since 1.8
 */
public final class Collectors {

  static final Set<Collector.Characteristics> CH_CONCURRENT_ID
      = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT,
      Collector.Characteristics.UNORDERED,
      Collector.Characteristics.IDENTITY_FINISH));
  static final Set<Collector.Characteristics> CH_CONCURRENT_NOID
      = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.CONCURRENT,
      Collector.Characteristics.UNORDERED));
  static final Set<Collector.Characteristics> CH_ID
      = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.IDENTITY_FINISH));
  static final Set<Collector.Characteristics> CH_UNORDERED_ID
      = Collections.unmodifiableSet(EnumSet.of(Collector.Characteristics.UNORDERED,
      Collector.Characteristics.IDENTITY_FINISH));
  static final Set<Collector.Characteristics> CH_NOID = Collections.emptySet();

  private Collectors() {
  }

  /**
   * Returns a merge function, suitable for use in
   * {@link Map#merge(Object, Object, BiFunction) Map.merge()} or
   * {@link #toMap(Function, Function, BinaryOperator) toMap()}, which always
   * throws {@code IllegalStateException}.  This can be used to enforce the
   * assumption that the elements being collected are distinct.
   *
   * @param <T> the type of input arguments to the merge function
   * @return a merge function which always throw {@code IllegalStateException}
   */
  private static <T> BinaryOperator<T> throwingMerger() {
    return (u, v) -> {
      throw new IllegalStateException(String.format("Duplicate key %s", u));
    };
  }

  @SuppressWarnings("unchecked")
  private static <I, R> Function<I, R> castingIdentity() {
    return i -> (R) i;
  }

  /**
   * Simple implementation class for {@code Collector}.
   *
   * @param <T> the type of elements to be collected
   * @param <R> the type of the result
   */
  static class CollectorImpl<T, A, R> implements Collector<T, A, R> {

    private final Supplier<A> supplier;
    private final BiConsumer<A, T> accumulator;
    private final BinaryOperator<A> combiner;
    private final Function<A, R> finisher;
    private final Set<Characteristics> characteristics;

    CollectorImpl(Supplier<A> supplier,
        BiConsumer<A, T> accumulator,
        BinaryOperator<A> combiner,
        Function<A, R> finisher,
        Set<Characteristics> characteristics) {
      this.supplier = supplier;
      this.accumulator = accumulator;
      this.combiner = combiner;
      this.finisher = finisher;
      this.characteristics = characteristics;
    }

    CollectorImpl(Supplier<A> supplier,
        BiConsumer<A, T> accumulator,
        BinaryOperator<A> combiner,
        Set<Characteristics> characteristics) {
      this(supplier, accumulator, combiner, castingIdentity(), characteristics);
    }

    @Override
    public BiConsumer<A, T> accumulator() {
      return accumulator;
    }

    @Override
    public Supplier<A> supplier() {
      return supplier;
    }

    @Override
    public BinaryOperator<A> combiner() {
      return combiner;
    }

    @Override
    public Function<A, R> finisher() {
      return finisher;
    }

    @Override
    public Set<Characteristics> characteristics() {
      return characteristics;
    }
  }

  /**
   * Returns a {@code Collector} that accumulates the input elements into a
   * new {@code Collection}, in encounter order.  The {@code Collection} is
   * created by the provided factory.
   *
   * @param <T> the type of the input elements
   * @param <C> the type of the resulting {@code Collection}
   * @param collectionFactory a {@code Supplier} which returns a new, empty {@code Collection} of
   * the appropriate type
   * @return a {@code Collector} which collects all the input elements into a {@code Collection}, in
   * encounter order
   */
  public static <T, C extends Collection<T>>
  Collector<T, ?, C> toCollection(Supplier<C> collectionFactory) {
    return new CollectorImpl<>(collectionFactory, Collection<T>::add,
        (r1, r2) -> {
          r1.addAll(r2);
          return r1;
        },
        CH_ID);
  }

  /**
   * Returns a {@code Collector} that accumulates the input elements into a
   * new {@code List}. There are no guarantees on the type, mutability,
   * serializability, or thread-safety of the {@code List} returned; if more
   * control over the returned {@code List} is required, use {@link #toCollection(Supplier)}.
   *
   * @param <T> the type of the input elements
   * @return a {@code Collector} which collects all the input elements into a {@code List}, in
   * encounter order
   */
  public static <T>
  Collector<T, ?, List<T>> toList() {
    return new CollectorImpl<>((Supplier<List<T>>) ArrayList::new, List::add,
        (left, right) -> {
          left.addAll(right);
          return left;
        },
        CH_ID);
  }

  /**
   * Returns a {@code Collector} that accumulates the input elements into a
   * new {@code Set}. There are no guarantees on the type, mutability,
   * serializability, or thread-safety of the {@code Set} returned; if more
   * control over the returned {@code Set} is required, use
   * {@link #toCollection(Supplier)}.
   *
   * <p>This is an {@link Collector.Characteristics#UNORDERED unordered}
   * Collector.
   *
   * @param <T> the type of the input elements
   * @return a {@code Collector} which collects all the input elements into a {@code Set}
   */
  public static <T>
  Collector<T, ?, Set<T>> toSet() {
    return new CollectorImpl<>((Supplier<Set<T>>) HashSet::new, Set::add,
        (left, right) -> {
          left.addAll(right);
          return left;
        },
        CH_UNORDERED_ID);
  }

  /**
   * Returns a {@code Collector} that concatenates the input elements into a
   * {@code String}, in encounter order.
   *
   * @return a {@code Collector} that concatenates the input elements into a {@code String}, in
   * encounter order
   */
  public static Collector<CharSequence, ?, String> joining() {
    return new CollectorImpl<CharSequence, StringBuilder, String>(
        StringBuilder::new, StringBuilder::append,
        (r1, r2) -> {
          r1.append(r2);
          return r1;
        },
        StringBuilder::toString, CH_NOID);
  }

  /**
   * Returns a {@code Collector} that concatenates the input elements,
   * separated by the specified delimiter, in encounter order.
   *
   * @param delimiter the delimiter to be used between each element
   * @return A {@code Collector} which concatenates CharSequence elements, separated by the
   * specified delimiter, in encounter order
   */
  public static Collector<CharSequence, ?, String> joining(CharSequence delimiter) {
    return joining(delimiter, "", "");
  }

  /**
   * Returns a {@code Collector} that concatenates the input elements,
   * separated by the specified delimiter, with the specified prefix and
   * suffix, in encounter order.
   *
   * @param delimiter the delimiter to be used between each element
   * @param prefix the sequence of characters to be used at the beginning of the joined result
   * @param suffix the sequence of characters to be used at the end of the joined result
   * @return A {@code Collector} which concatenates CharSequence elements, separated by the
   * specified delimiter, in encounter order
   */
  public static Collector<CharSequence, ?, String> joining(CharSequence delimiter,
      CharSequence prefix,
      CharSequence suffix) {
    return new CollectorImpl<>(
        () -> new StringJoiner(delimiter, prefix, suffix),
        StringJoiner::add, StringJoiner::merge,
        StringJoiner::toString, CH_NOID);
  }

  /**
   * {@code BinaryOperator<Map>} that merges the contents of its right
   * argument into its left argument, using the provided merge function to
   * handle duplicate keys.
   *
   * @param <K> type of the map keys
   * @param <V> type of the map values
   * @param <M> type of the map
   * @param mergeFunction A merge function suitable for {@link Map#merge(Object, Object, BiFunction)
   * Map.merge()}
   * @return a merge function for two maps
   */
  private static <K, V, M extends Map<K, V>>
  BinaryOperator<M> mapMerger(BinaryOperator<V> mergeFunction) {
    return (m1, m2) -> {
      for (Map.Entry<K, V> e : m2.entrySet()) {
        m1.merge(e.getKey(), e.getValue(), mergeFunction);
      }
      return m1;
    };
  }

  /**
   * Adapts a {@code Collector} accepting elements of type {@code U} to one
   * accepting elements of type {@code T} by applying a mapping function to
   * each input element before accumulation.
   *
   * @param <T> the type of the input elements
   * @param <U> type of elements accepted by downstream collector
   * @param <A> intermediate accumulation type of the downstream collector
   * @param <R> result type of collector
   * @param mapper a function to be applied to the input elements
   * @param downstream a collector which will accept mapped values
   * @return a collector which applies the mapping function to the input elements and provides the
   * mapped results to the downstream collector
   * @apiNote The {@code mapping()} collectors are most useful when used in a multi-level reduction,
   * such as downstream of a {@code groupingBy} or {@code partitioningBy}.  For example, given a
   * stream of {@code Person}, to accumulate the set of last names in each city:
   * <pre>{@code
   *     Map<City, Set<String>> lastNamesByCity
   *         = people.stream().collect(groupingBy(Person::getCity,
   *                                              mapping(Person::getLastName, toSet())));
   * }</pre>
   */
  public static <T, U, A, R>
  Collector<T, ?, R> mapping(Function<? super T, ? extends U> mapper,
      Collector<? super U, A, R> downstream) {
    BiConsumer<A, ? super U> downstreamAccumulator = downstream.accumulator();
    return new CollectorImpl<>(downstream.supplier(),
        (r, t) -> downstreamAccumulator.accept(r, mapper.apply(t)),
        downstream.combiner(), downstream.finisher(),
        downstream.characteristics());
  }

  /**
   * Adapts a {@code Collector} to perform an additional finishing
   * transformation.  For example, one could adapt the {@link #toList()}
   * collector to always produce an immutable list with:
   * <pre>{@code
   *     List<String> people
   *         = people.stream().collect(collectingAndThen(toList(), Collections::unmodifiableList));
   * }</pre>
   *
   * @param <T> the type of the input elements
   * @param <A> intermediate accumulation type of the downstream collector
   * @param <R> result type of the downstream collector
   * @param <RR> result type of the resulting collector
   * @param downstream a collector
   * @param finisher a function to be applied to the final result of the downstream collector
   * @return a collector which performs the action of the downstream collector, followed by an
   * additional finishing step
   */
  public static <T, A, R, RR> Collector<T, A, RR> collectingAndThen(Collector<T, A, R> downstream,
      Function<R, RR> finisher) {
    Set<Collector.Characteristics> characteristics = downstream.characteristics();
    if (characteristics.contains(Collector.Characteristics.IDENTITY_FINISH)) {
      if (characteristics.size() == 1) {
        characteristics = Collectors.CH_NOID;
      } else {
        characteristics = EnumSet.copyOf(characteristics);
        characteristics.remove(Collector.Characteristics.IDENTITY_FINISH);
        characteristics = Collections.unmodifiableSet(characteristics);
      }
    }
    return new CollectorImpl<>(downstream.supplier(),
        downstream.accumulator(),
        downstream.combiner(),
        downstream.finisher().andThen(finisher),
        characteristics);
  }

  /**
   * Returns a {@code Collector} accepting elements of type {@code T} that
   * counts the number of input elements.  If no elements are present, the
   * result is 0.
   *
   * @param <T> the type of the input elements
   * @return a {@code Collector} that counts the input elements
   * @implSpec This produces a result equivalent to:
   * <pre>{@code
   *     reducing(0L, e -> 1L, Long::sum)
   * }</pre>
   */
  public static <T> Collector<T, ?, Long>
  counting() {
    return reducing(0L, e -> 1L, Long::sum);
  }

  /**
   * Returns a {@code Collector} that produces the minimal element according
   * to a given {@code Comparator}, described as an {@code Optional<T>}.
   *
   * @param <T> the type of the input elements
   * @param comparator a {@code Comparator} for comparing elements
   * @return a {@code Collector} that produces the minimal value
   * @implSpec This produces a result equivalent to:
   * <pre>{@code
   *     reducing(BinaryOperator.minBy(comparator))
   * }</pre>
   */
  public static <T> Collector<T, ?, Optional<T>>
  minBy(Comparator<? super T> comparator) {
    return reducing(BinaryOperator.minBy(comparator));
  }

  /**
   * Returns a {@code Collector} that produces the maximal element according
   * to a given {@code Comparator}, described as an {@code Optional<T>}.
   *
   * @param <T> the type of the input elements
   * @param comparator a {@code Comparator} for comparing elements
   * @return a {@code Collector} that produces the maximal value
   * @implSpec This produces a result equivalent to:
   * <pre>{@code
   *     reducing(BinaryOperator.maxBy(comparator))
   * }</pre>
   */
  public static <T> Collector<T, ?, Optional<T>>
  maxBy(Comparator<? super T> comparator) {
    return reducing(BinaryOperator.maxBy(comparator));
  }

  /**
   * Returns a {@code Collector} that produces the sum of a integer-valued
   * function applied to the input elements.  If no elements are present,
   * the result is 0.
   *
   * @param <T> the type of the input elements
   * @param mapper a function extracting the property to be summed
   * @return a {@code Collector} that produces the sum of a derived property
   */
  public static <T> Collector<T, ?, Integer>
  summingInt(ToIntFunction<? super T> mapper) {
    return new CollectorImpl<>(
        () -> new int[1],
        (a, t) -> {
          a[0] += mapper.applyAsInt(t);
        },
        (a, b) -> {
          a[0] += b[0];
          return a;
        },
        a -> a[0], CH_NOID);
  }

  /**
   * Returns a {@code Collector} that produces the sum of a long-valued
   * function applied to the input elements.  If no elements are present,
   * the result is 0.
   *
   * @param <T> the type of the input elements
   * @param mapper a function extracting the property to be summed
   * @return a {@code Collector} that produces the sum of a derived property
   */
  public static <T> Collector<T, ?, Long>
  summingLong(ToLongFunction<? super T> mapper) {
    return new CollectorImpl<>(
        () -> new long[1],
        (a, t) -> {
          a[0] += mapper.applyAsLong(t);
        },
        (a, b) -> {
          a[0] += b[0];
          return a;
        },
        a -> a[0], CH_NOID);
  }

  /**
   * Returns a {@code Collector} that produces the sum of a double-valued
   * function applied to the input elements.  If no elements are present,
   * the result is 0.
   *
   * <p>The sum returned can vary depending upon the order in which
   * values are recorded, due to accumulated rounding error in
   * addition of values of differing magnitudes. Values sorted by increasing
   * absolute magnitude tend to yield more accurate results.  If any recorded
   * value is a {@code NaN} or the sum is at any point a {@code NaN} then the
   * sum will be {@code NaN}.
   *
   * @param <T> the type of the input elements
   * @param mapper a function extracting the property to be summed
   * @return a {@code Collector} that produces the sum of a derived property
   */
  public static <T> Collector<T, ?, Double>
  summingDouble(ToDoubleFunction<? super T> mapper) {
        /*
         * In the arrays allocated for the collect operation, index 0
         * holds the high-order bits of the running sum, index 1 holds
         * the low-order bits of the sum computed via compensated
         * summation, and index 2 holds the simple sum used to compute
         * the proper result if the stream contains infinite values of
         * the same sign.
         */
    return new CollectorImpl<>(
        () -> new double[3],
        (a, t) -> {
          sumWithCompensation(a, mapper.applyAsDouble(t));
          a[2] += mapper.applyAsDouble(t);
        },
        (a, b) -> {
          sumWithCompensation(a, b[0]);
          a[2] += b[2];
          return sumWithCompensation(a, b[1]);
        },
        a -> computeFinalSum(a),
        CH_NOID);
  }

  /**
   * Incorporate a new double value using Kahan summation /
   * compensation summation.
   *
   * High-order bits of the sum are in intermediateSum[0], low-order
   * bits of the sum are in intermediateSum[1], any additional
   * elements are application-specific.
   *
   * @param intermediateSum the high-order and low-order words of the intermediate sum
   * @param value the name value to be included in the running sum
   */
  static double[] sumWithCompensation(double[] intermediateSum, double value) {
    double tmp = value - intermediateSum[1];
    double sum = intermediateSum[0];
    double velvel = sum + tmp; // Little wolf of rounding error
    intermediateSum[1] = (velvel - sum) - tmp;
    intermediateSum[0] = velvel;
    return intermediateSum;
  }

  /**
   * If the compensated sum is spuriously NaN from accumulating one
   * or more same-signed infinite values, return the
   * correctly-signed infinity stored in the simple sum.
   */
  static double computeFinalSum(double[] summands) {
    // Better error bounds to add both terms as the final sum
    double tmp = summands[0] + summands[1];
    double simpleSum = summands[summands.length - 1];
    if (Double.isNaN(tmp) && Double.isInfinite(simpleSum)) {
      return simpleSum;
    } else {
      return tmp;
    }
  }

  /**
   * Returns a {@code Collector} that produces the arithmetic mean of an integer-valued
   * function applied to the input elements.  If no elements are present,
   * the result is 0.
   *
   * @param <T> the type of the input elements
   * @param mapper a function extracting the property to be summed
   * @return a {@code Collector} that produces the sum of a derived property
   */
  public static <T> Collector<T, ?, Double>
  averagingInt(ToIntFunction<? super T> mapper) {
    return new CollectorImpl<>(
        () -> new long[2],
        (a, t) -> {
          a[0] += mapper.applyAsInt(t);
          a[1]++;
        },
        (a, b) -> {
          a[0] += b[0];
          a[1] += b[1];
          return a;
        },
        a -> (a[1] == 0) ? 0.0d : (double) a[0] / a[1], CH_NOID);
  }

  /**
   * Returns a {@code Collector} that produces the arithmetic mean of a long-valued
   * function applied to the input elements.  If no elements are present,
   * the result is 0.
   *
   * @param <T> the type of the input elements
   * @param mapper a function extracting the property to be summed
   * @return a {@code Collector} that produces the sum of a derived property
   */
  public static <T> Collector<T, ?, Double>
  averagingLong(ToLongFunction<? super T> mapper) {
    return new CollectorImpl<>(
        () -> new long[2],
        (a, t) -> {
          a[0] += mapper.applyAsLong(t);
          a[1]++;
        },
        (a, b) -> {
          a[0] += b[0];
          a[1] += b[1];
          return a;
        },
        a -> (a[1] == 0) ? 0.0d : (double) a[0] / a[1], CH_NOID);
  }

  /**
   * Returns a {@code Collector} that produces the arithmetic mean of a double-valued
   * function applied to the input elements.  If no elements are present,
   * the result is 0.
   *
   * <p>The average returned can vary depending upon the order in which
   * values are recorded, due to accumulated rounding error in
   * addition of values of differing magnitudes. Values sorted by increasing
   * absolute magnitude tend to yield more accurate results.  If any recorded
   * value is a {@code NaN} or the sum is at any point a {@code NaN} then the
   * average will be {@code NaN}.
   *
   * @param <T> the type of the input elements
   * @param mapper a function extracting the property to be summed
   * @return a {@code Collector} that produces the sum of a derived property
   * @implNote The {@code double} format can represent all consecutive integers in the range
   * -2<sup>53</sup> to 2<sup>53</sup>. If the pipeline has more than 2<sup>53</sup> values, the
   * divisor in the average computation will saturate at 2<sup>53</sup>, leading to additional
   * numerical errors.
   */
  public static <T> Collector<T, ?, Double>
  averagingDouble(ToDoubleFunction<? super T> mapper) {
        /*
         * In the arrays allocated for the collect operation, index 0
         * holds the high-order bits of the running sum, index 1 holds
         * the low-order bits of the sum computed via compensated
         * summation, and index 2 holds the number of values seen.
         */
    return new CollectorImpl<>(
        () -> new double[4],
        (a, t) -> {
          sumWithCompensation(a, mapper.applyAsDouble(t));
          a[2]++;
          a[3] += mapper.applyAsDouble(t);
        },
        (a, b) -> {
          sumWithCompensation(a, b[0]);
          sumWithCompensation(a, b[1]);
          a[2] += b[2];
          a[3] += b[3];
          return a;
        },
        a -> (a[2] == 0) ? 0.0d : (computeFinalSum(a) / a[2]),
        CH_NOID);
  }

  /**
   * Returns a {@code Collector} which performs a reduction of its
   * input elements under a specified {@code BinaryOperator} using the
   * provided identity.
   *
   * @param <T> element type for the input and output of the reduction
   * @param identity the identity value for the reduction (also, the value that is returned when
   * there are no input elements)
   * @param op a {@code BinaryOperator<T>} used to reduce the input elements
   * @return a {@code Collector} which implements the reduction operation
   * @apiNote The {@code reducing()} collectors are most useful when used in a multi-level
   * reduction, downstream of {@code groupingBy} or {@code partitioningBy}.  To perform a simple
   * reduction on a stream, use {@link Stream#reduce(Object, BinaryOperator)}} instead.
   * @see #reducing(BinaryOperator)
   * @see #reducing(Object, Function, BinaryOperator)
   */
  public static <T> Collector<T, ?, T>
  reducing(T identity, BinaryOperator<T> op) {
    return new CollectorImpl<>(
        boxSupplier(identity),
        (a, t) -> {
          a[0] = op.apply(a[0], t);
        },
        (a, b) -> {
          a[0] = op.apply(a[0], b[0]);
          return a;
        },
        a -> a[0],
        CH_NOID);
  }

  @SuppressWarnings("unchecked")
  private static <T> Supplier<T[]> boxSupplier(T identity) {
    return () -> (T[]) new Object[]{identity};
  }

  /**
   * Returns a {@code Collector} which performs a reduction of its
   * input elements under a specified {@code BinaryOperator}.  The result
   * is described as an {@code Optional<T>}.
   *
   * @param <T> element type for the input and output of the reduction
   * @param op a {@code BinaryOperator<T>} used to reduce the input elements
   * @return a {@code Collector} which implements the reduction operation
   * @apiNote The {@code reducing()} collectors are most useful when used in a multi-level
   * reduction, downstream of {@code groupingBy} or {@code partitioningBy}.  To perform a simple
   * reduction on a stream, use {@link Stream#reduce(BinaryOperator)} instead.
   *
   * <p>For example, given a stream of {@code Person}, to calculate tallest person in each city:
   * <pre>{@code
   *     Comparator<Person> byHeight = Comparator.comparing(Person::getHeight);
   *     Map<City, Person> tallestByCity
   *         = people.stream().collect(groupingBy(Person::getCity, reducing(BinaryOperator.maxBy(byHeight))));
   * }</pre>
   * @see #reducing(Object, BinaryOperator)
   * @see #reducing(Object, Function, BinaryOperator)
   */
  public static <T> Collector<T, ?, Optional<T>>
  reducing(BinaryOperator<T> op) {
    class OptionalBox implements Consumer<T> {

      T value = null;
      boolean present = false;

      @Override
      public void accept(T t) {
        if (present) {
          value = op.apply(value, t);
        } else {
          value = t;
          present = true;
        }
      }
    }

    return new CollectorImpl<T, OptionalBox, Optional<T>>(
        OptionalBox::new, OptionalBox::accept,
        (a, b) -> {
          if (b.present) {
            a.accept(b.value);
          }
          return a;
        },
        a -> Optional.ofNullable(a.value), CH_NOID);
  }

  /**
   * Returns a {@code Collector} which performs a reduction of its
   * input elements under a specified mapping function and
   * {@code BinaryOperator}. This is a generalization of
   * {@link #reducing(Object, BinaryOperator)} which allows a transformation
   * of the elements before reduction.
   *
   * @param <T> the type of the input elements
   * @param <U> the type of the mapped values
   * @param identity the identity value for the reduction (also, the value that is returned when
   * there are no input elements)
   * @param mapper a mapping function to apply to each input value
   * @param op a {@code BinaryOperator<U>} used to reduce the mapped values
   * @return a {@code Collector} implementing the map-reduce operation
   * @apiNote The {@code reducing()} collectors are most useful when used in a multi-level
   * reduction, downstream of {@code groupingBy} or {@code partitioningBy}.  To perform a simple
   * map-reduce on a stream, use {@link Stream#map(Function)} and {@link Stream#reduce(Object,
   * BinaryOperator)} instead.
   *
   * <p>For example, given a stream of {@code Person}, to calculate the longest last name of
   * residents in each city:
   * <pre>{@code
   *     Comparator<String> byLength = Comparator.comparing(String::length);
   *     Map<City, String> longestLastNameByCity
   *         = people.stream().collect(groupingBy(Person::getCity,
   *                                              reducing(Person::getLastName,
   * BinaryOperator.maxBy(byLength))));
   * }</pre>
   * @see #reducing(Object, BinaryOperator)
   * @see #reducing(BinaryOperator)
   */
  public static <T, U>
  Collector<T, ?, U> reducing(U identity,
      Function<? super T, ? extends U> mapper,
      BinaryOperator<U> op) {
    return new CollectorImpl<>(
        boxSupplier(identity),
        (a, t) -> {
          a[0] = op.apply(a[0], mapper.apply(t));
        },
        (a, b) -> {
          a[0] = op.apply(a[0], b[0]);
          return a;
        },
        a -> a[0], CH_NOID);
  }

  /**
   * Returns a {@code Collector} implementing a "group by" operation on
   * input elements of type {@code T}, grouping elements according to a
   * classification function, and returning the results in a {@code Map}.
   *
   * <p>The classification function maps elements to some key type {@code K}.
   * The collector produces a {@code Map<K, List<T>>} whose keys are the
   * values resulting from applying the classification function to the input
   * elements, and whose corresponding values are {@code List}s containing the
   * input elements which map to the associated key under the classification
   * function.
   *
   * <p>There are no guarantees on the type, mutability, serializability, or
   * thread-safety of the {@code Map} or {@code List} objects returned.
   *
   * @param <T> the type of the input elements
   * @param <K> the type of the keys
   * @param classifier the classifier function mapping input elements to keys
   * @return a {@code Collector} implementing the group-by operation
   * @implSpec This produces a result similar to:
   * <pre>{@code
   *     groupingBy(classifier, toList());
   * }</pre>
   * @implNote The returned {@code Collector} is not concurrent.  For parallel stream pipelines, the
   * {@code combiner} function operates by merging the keys from one map into another, which can be
   * an expensive operation.  If preservation of the order in which elements appear in the resulting
   * {@code Map} collector is not required, using {@link #groupingByConcurrent(Function)} may offer
   * better parallel performance.
   * @see #groupingBy(Function, Collector)
   * @see #groupingBy(Function, Supplier, Collector)
   * @see #groupingByConcurrent(Function)
   */
  public static <T, K> Collector<T, ?, Map<K, List<T>>>
  groupingBy(Function<? super T, ? extends K> classifier) {
    return groupingBy(classifier, toList());
  }

  /**
   * Returns a {@code Collector} implementing a cascaded "group by" operation
   * on input elements of type {@code T}, grouping elements according to a
   * classification function, and then performing a reduction operation on
   * the values associated with a given key using the specified downstream
   * {@code Collector}.
   *
   * <p>The classification function maps elements to some key type {@code K}.
   * The downstream collector operates on elements of type {@code T} and
   * produces a result of type {@code D}. The resulting collector produces a
   * {@code Map<K, D>}.
   *
   * <p>There are no guarantees on the type, mutability,
   * serializability, or thread-safety of the {@code Map} returned.
   *
   * <p>For example, to compute the set of last names of people in each city:
   * <pre>{@code
   *     Map<City, Set<String>> namesByCity
   *         = people.stream().collect(groupingBy(Person::getCity,
   *                                              mapping(Person::getLastName, toSet())));
   * }</pre>
   *
   * @param <T> the type of the input elements
   * @param <K> the type of the keys
   * @param <A> the intermediate accumulation type of the downstream collector
   * @param <D> the result type of the downstream reduction
   * @param classifier a classifier function mapping input elements to keys
   * @param downstream a {@code Collector} implementing the downstream reduction
   * @return a {@code Collector} implementing the cascaded group-by operation
   * @implNote The returned {@code Collector} is not concurrent.  For parallel stream pipelines, the
   * {@code combiner} function operates by merging the keys from one map into another, which can be
   * an expensive operation.  If preservation of the order in which elements are presented to the
   * downstream collector is not required, using {@link #groupingByConcurrent(Function, Collector)}
   * may offer better parallel performance.
   * @see #groupingBy(Function)
   * @see #groupingBy(Function, Supplier, Collector)
   * @see #groupingByConcurrent(Function, Collector)
   */
  public static <T, K, A, D>
  Collector<T, ?, Map<K, D>> groupingBy(Function<? super T, ? extends K> classifier,
      Collector<? super T, A, D> downstream) {
    return groupingBy(classifier, HashMap::new, downstream);
  }

  /**
   * Returns a {@code Collector} implementing a cascaded "group by" operation
   * on input elements of type {@code T}, grouping elements according to a
   * classification function, and then performing a reduction operation on
   * the values associated with a given key using the specified downstream
   * {@code Collector}.  The {@code Map} produced by the Collector is created
   * with the supplied factory function.
   *
   * <p>The classification function maps elements to some key type {@code K}.
   * The downstream collector operates on elements of type {@code T} and
   * produces a result of type {@code D}. The resulting collector produces a
   * {@code Map<K, D>}.
   *
   * <p>For example, to compute the set of last names of people in each city,
   * where the city names are sorted:
   * <pre>{@code
   *     Map<City, Set<String>> namesByCity
   *         = people.stream().collect(groupingBy(Person::getCity, TreeMap::new,
   *                                              mapping(Person::getLastName, toSet())));
   * }</pre>
   *
   * @param <T> the type of the input elements
   * @param <K> the type of the keys
   * @param <A> the intermediate accumulation type of the downstream collector
   * @param <D> the result type of the downstream reduction
   * @param <M> the type of the resulting {@code Map}
   * @param classifier a classifier function mapping input elements to keys
   * @param downstream a {@code Collector} implementing the downstream reduction
   * @param mapFactory a function which, when called, produces a new empty {@code Map} of the
   * desired type
   * @return a {@code Collector} implementing the cascaded group-by operation
   * @implNote The returned {@code Collector} is not concurrent.  For parallel stream pipelines, the
   * {@code combiner} function operates by merging the keys from one map into another, which can be
   * an expensive operation.  If preservation of the order in which elements are presented to the
   * downstream collector is not required, using {@link #groupingByConcurrent(Function, Supplier,
   * Collector)} may offer better parallel performance.
   * @see #groupingBy(Function, Collector)
   * @see #groupingBy(Function)
   * @see #groupingByConcurrent(Function, Supplier, Collector)
   */
  public static <T, K, D, A, M extends Map<K, D>>
  Collector<T, ?, M> groupingBy(Function<? super T, ? extends K> classifier,
      Supplier<M> mapFactory,
      Collector<? super T, A, D> downstream) {
    Supplier<A> downstreamSupplier = downstream.supplier();
    BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
    BiConsumer<Map<K, A>, T> accumulator = (m, t) -> {
      K key = Objects.requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
      A container = m.computeIfAbsent(key, k -> downstreamSupplier.get());
      downstreamAccumulator.accept(container, t);
    };
    BinaryOperator<Map<K, A>> merger = Collectors.<K, A, Map<K, A>>mapMerger(downstream.combiner());
    @SuppressWarnings("unchecked")
    Supplier<Map<K, A>> mangledFactory = (Supplier<Map<K, A>>) mapFactory;

    if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
      return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_ID);
    } else {
      @SuppressWarnings("unchecked")
      Function<A, A> downstreamFinisher = (Function<A, A>) downstream.finisher();
      Function<Map<K, A>, M> finisher = intermediate -> {
        intermediate.replaceAll((k, v) -> downstreamFinisher.apply(v));
        @SuppressWarnings("unchecked")
        M castResult = (M) intermediate;
        return castResult;
      };
      return new CollectorImpl<>(mangledFactory, accumulator, merger, finisher, CH_NOID);
    }
  }

  /**
   * Returns a concurrent {@code Collector} implementing a "group by"
   * operation on input elements of type {@code T}, grouping elements
   * according to a classification function.
   *
   * <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
   * {@link Collector.Characteristics#UNORDERED unordered} Collector.
   *
   * <p>The classification function maps elements to some key type {@code K}.
   * The collector produces a {@code ConcurrentMap<K, List<T>>} whose keys are the
   * values resulting from applying the classification function to the input
   * elements, and whose corresponding values are {@code List}s containing the
   * input elements which map to the associated key under the classification
   * function.
   *
   * <p>There are no guarantees on the type, mutability, or serializability
   * of the {@code Map} or {@code List} objects returned, or of the
   * thread-safety of the {@code List} objects returned.
   *
   * @param <T> the type of the input elements
   * @param <K> the type of the keys
   * @param classifier a classifier function mapping input elements to keys
   * @return a concurrent, unordered {@code Collector} implementing the group-by operation
   * @implSpec This produces a result similar to:
   * <pre>{@code
   *     groupingByConcurrent(classifier, toList());
   * }</pre>
   * @see #groupingBy(Function)
   * @see #groupingByConcurrent(Function, Collector)
   * @see #groupingByConcurrent(Function, Supplier, Collector)
   */
  public static <T, K>
  Collector<T, ?, ConcurrentMap<K, List<T>>>
  groupingByConcurrent(Function<? super T, ? extends K> classifier) {
    return groupingByConcurrent(classifier, ConcurrentHashMap::new, toList());
  }

  /**
   * Returns a concurrent {@code Collector} implementing a cascaded "group by"
   * operation on input elements of type {@code T}, grouping elements
   * according to a classification function, and then performing a reduction
   * operation on the values associated with a given key using the specified
   * downstream {@code Collector}.
   *
   * <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
   * {@link Collector.Characteristics#UNORDERED unordered} Collector.
   *
   * <p>The classification function maps elements to some key type {@code K}.
   * The downstream collector operates on elements of type {@code T} and
   * produces a result of type {@code D}. The resulting collector produces a
   * {@code Map<K, D>}.
   *
   * <p>For example, to compute the set of last names of people in each city,
   * where the city names are sorted:
   * <pre>{@code
   *     ConcurrentMap<City, Set<String>> namesByCity
   *         = people.stream().collect(groupingByConcurrent(Person::getCity,
   *                                                        mapping(Person::getLastName, toSet())));
   * }</pre>
   *
   * @param <T> the type of the input elements
   * @param <K> the type of the keys
   * @param <A> the intermediate accumulation type of the downstream collector
   * @param <D> the result type of the downstream reduction
   * @param classifier a classifier function mapping input elements to keys
   * @param downstream a {@code Collector} implementing the downstream reduction
   * @return a concurrent, unordered {@code Collector} implementing the cascaded group-by operation
   * @see #groupingBy(Function, Collector)
   * @see #groupingByConcurrent(Function)
   * @see #groupingByConcurrent(Function, Supplier, Collector)
   */
  public static <T, K, A, D>
  Collector<T, ?, ConcurrentMap<K, D>> groupingByConcurrent(
      Function<? super T, ? extends K> classifier,
      Collector<? super T, A, D> downstream) {
    return groupingByConcurrent(classifier, ConcurrentHashMap::new, downstream);
  }

  /**
   * Returns a concurrent {@code Collector} implementing a cascaded "group by"
   * operation on input elements of type {@code T}, grouping elements
   * according to a classification function, and then performing a reduction
   * operation on the values associated with a given key using the specified
   * downstream {@code Collector}.  The {@code ConcurrentMap} produced by the
   * Collector is created with the supplied factory function.
   *
   * <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
   * {@link Collector.Characteristics#UNORDERED unordered} Collector.
   *
   * <p>The classification function maps elements to some key type {@code K}.
   * The downstream collector operates on elements of type {@code T} and
   * produces a result of type {@code D}. The resulting collector produces a
   * {@code Map<K, D>}.
   *
   * <p>For example, to compute the set of last names of people in each city,
   * where the city names are sorted:
   * <pre>{@code
   *     ConcurrentMap<City, Set<String>> namesByCity
   *         = people.stream().collect(groupingBy(Person::getCity, ConcurrentSkipListMap::new,
   *                                              mapping(Person::getLastName, toSet())));
   * }</pre>
   *
   * @param <T> the type of the input elements
   * @param <K> the type of the keys
   * @param <A> the intermediate accumulation type of the downstream collector
   * @param <D> the result type of the downstream reduction
   * @param <M> the type of the resulting {@code ConcurrentMap}
   * @param classifier a classifier function mapping input elements to keys
   * @param downstream a {@code Collector} implementing the downstream reduction
   * @param mapFactory a function which, when called, produces a new empty {@code ConcurrentMap} of
   * the desired type
   * @return a concurrent, unordered {@code Collector} implementing the cascaded group-by operation
   * @see #groupingByConcurrent(Function)
   * @see #groupingByConcurrent(Function, Collector)
   * @see #groupingBy(Function, Supplier, Collector)
   */
  public static <T, K, A, D, M extends ConcurrentMap<K, D>>
  Collector<T, ?, M> groupingByConcurrent(Function<? super T, ? extends K> classifier,
      Supplier<M> mapFactory,
      Collector<? super T, A, D> downstream) {
    Supplier<A> downstreamSupplier = downstream.supplier();
    BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
    BinaryOperator<ConcurrentMap<K, A>> merger = Collectors.<K, A, ConcurrentMap<K, A>>mapMerger(
        downstream.combiner());
    @SuppressWarnings("unchecked")
    Supplier<ConcurrentMap<K, A>> mangledFactory = (Supplier<ConcurrentMap<K, A>>) mapFactory;
    BiConsumer<ConcurrentMap<K, A>, T> accumulator;
    if (downstream.characteristics().contains(Collector.Characteristics.CONCURRENT)) {
      accumulator = (m, t) -> {
        K key = Objects
            .requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
        A resultContainer = m.computeIfAbsent(key, k -> downstreamSupplier.get());
        downstreamAccumulator.accept(resultContainer, t);
      };
    } else {
      accumulator = (m, t) -> {
        K key = Objects
            .requireNonNull(classifier.apply(t), "element cannot be mapped to a null key");
        A resultContainer = m.computeIfAbsent(key, k -> downstreamSupplier.get());
        synchronized (resultContainer) {
          downstreamAccumulator.accept(resultContainer, t);
        }
      };
    }

    if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
      return new CollectorImpl<>(mangledFactory, accumulator, merger, CH_CONCURRENT_ID);
    } else {
      @SuppressWarnings("unchecked")
      Function<A, A> downstreamFinisher = (Function<A, A>) downstream.finisher();
      Function<ConcurrentMap<K, A>, M> finisher = intermediate -> {
        intermediate.replaceAll((k, v) -> downstreamFinisher.apply(v));
        @SuppressWarnings("unchecked")
        M castResult = (M) intermediate;
        return castResult;
      };
      return new CollectorImpl<>(mangledFactory, accumulator, merger, finisher, CH_CONCURRENT_NOID);
    }
  }

  /**
   * Returns a {@code Collector} which partitions the input elements according
   * to a {@code Predicate}, and organizes them into a
   * {@code Map<Boolean, List<T>>}.
   *
   * There are no guarantees on the type, mutability,
   * serializability, or thread-safety of the {@code Map} returned.
   *
   * @param <T> the type of the input elements
   * @param predicate a predicate used for classifying input elements
   * @return a {@code Collector} implementing the partitioning operation
   * @see #partitioningBy(Predicate, Collector)
   */
  public static <T>
  Collector<T, ?, Map<Boolean, List<T>>> partitioningBy(Predicate<? super T> predicate) {
    return partitioningBy(predicate, toList());
  }

  /**
   * Returns a {@code Collector} which partitions the input elements according
   * to a {@code Predicate}, reduces the values in each partition according to
   * another {@code Collector}, and organizes them into a
   * {@code Map<Boolean, D>} whose values are the result of the downstream
   * reduction.
   *
   * <p>There are no guarantees on the type, mutability,
   * serializability, or thread-safety of the {@code Map} returned.
   *
   * @param <T> the type of the input elements
   * @param <A> the intermediate accumulation type of the downstream collector
   * @param <D> the result type of the downstream reduction
   * @param predicate a predicate used for classifying input elements
   * @param downstream a {@code Collector} implementing the downstream reduction
   * @return a {@code Collector} implementing the cascaded partitioning operation
   * @see #partitioningBy(Predicate)
   */
  public static <T, D, A>
  Collector<T, ?, Map<Boolean, D>> partitioningBy(Predicate<? super T> predicate,
      Collector<? super T, A, D> downstream) {
    BiConsumer<A, ? super T> downstreamAccumulator = downstream.accumulator();
    BiConsumer<Partition<A>, T> accumulator = (result, t) ->
        downstreamAccumulator.accept(predicate.test(t) ? result.forTrue : result.forFalse, t);
    BinaryOperator<A> op = downstream.combiner();
    BinaryOperator<Partition<A>> merger = (left, right) ->
        new Partition<>(op.apply(left.forTrue, right.forTrue),
            op.apply(left.forFalse, right.forFalse));
    Supplier<Partition<A>> supplier = () ->
        new Partition<>(downstream.supplier().get(),
            downstream.supplier().get());
    if (downstream.characteristics().contains(Collector.Characteristics.IDENTITY_FINISH)) {
      return new CollectorImpl<>(supplier, accumulator, merger, CH_ID);
    } else {
      Function<Partition<A>, Map<Boolean, D>> finisher = par ->
          new Partition<>(downstream.finisher().apply(par.forTrue),
              downstream.finisher().apply(par.forFalse));
      return new CollectorImpl<>(supplier, accumulator, merger, finisher, CH_NOID);
    }
  }

  /**
   * Returns a {@code Collector} that accumulates elements into a
   * {@code Map} whose keys and values are the result of applying the provided
   * mapping functions to the input elements.
   *
   * <p>If the mapped keys contains duplicates (according to
   * {@link Object#equals(Object)}), an {@code IllegalStateException} is
   * thrown when the collection operation is performed.  If the mapped keys
   * may have duplicates, use {@link #toMap(Function, Function, BinaryOperator)}
   * instead.
   *
   * @param <T> the type of the input elements
   * @param <K> the output type of the key mapping function
   * @param <U> the output type of the value mapping function
   * @param keyMapper a mapping function to produce keys
   * @param valueMapper a mapping function to produce values
   * @return a {@code Collector} which collects elements into a {@code Map} whose keys and values
   * are the result of applying mapping functions to the input elements
   * @apiNote It is common for either the key or the value to be the input elements. In this case,
   * the utility method {@link java.util.function.Function#identity()} may be helpful. For example,
   * the following produces a {@code Map} mapping students to their grade point average:
   * <pre>{@code
   *     Map<Student, Double> studentToGPA
   *         students.stream().collect(toMap(Functions.identity(),
   *                                         student -> computeGPA(student)));
   * }</pre>
   * And the following produces a {@code Map} mapping a unique identifier to students:
   * <pre>{@code
   *     Map<String, Student> studentIdToStudent
   *         students.stream().collect(toMap(Student::getId,
   *                                         Functions.identity());
   * }</pre>
   * @implNote The returned {@code Collector} is not concurrent.  For parallel stream pipelines, the
   * {@code combiner} function operates by merging the keys from one map into another, which can be
   * an expensive operation.  If it is not required that results are inserted into the {@code Map}
   * in encounter order, using {@link #toConcurrentMap(Function, Function)} may offer better
   * parallel performance.
   * @see #toMap(Function, Function, BinaryOperator)
   * @see #toMap(Function, Function, BinaryOperator, Supplier)
   * @see #toConcurrentMap(Function, Function)
   */
  public static <T, K, U>
  Collector<T, ?, Map<K, U>> toMap(Function<? super T, ? extends K> keyMapper,
      Function<? super T, ? extends U> valueMapper) {
    return toMap(keyMapper, valueMapper, throwingMerger(), HashMap::new);
  }

  /**
   * Returns a {@code Collector} that accumulates elements into a
   * {@code Map} whose keys and values are the result of applying the provided
   * mapping functions to the input elements.
   *
   * <p>If the mapped
   * keys contains duplicates (according to {@link Object#equals(Object)}),
   * the value mapping function is applied to each equal element, and the
   * results are merged using the provided merging function.
   *
   * @param <T> the type of the input elements
   * @param <K> the output type of the key mapping function
   * @param <U> the output type of the value mapping function
   * @param keyMapper a mapping function to produce keys
   * @param valueMapper a mapping function to produce values
   * @param mergeFunction a merge function, used to resolve collisions between values associated
   * with the same key, as supplied to {@link Map#merge(Object, Object, BiFunction)}
   * @return a {@code Collector} which collects elements into a {@code Map} whose keys are the
   * result of applying a key mapping function to the input elements, and whose values are the
   * result of applying a value mapping function to all input elements equal to the key and
   * combining them using the merge function
   * @apiNote There are multiple ways to deal with collisions between multiple elements mapping to
   * the same key.  The other forms of {@code toMap} simply use a merge function that throws
   * unconditionally, but you can easily write more flexible merge policies.  For example, if you
   * have a stream of {@code Person}, and you want to produce a "phone book" mapping name to
   * address, but it is possible that two persons have the same name, you can do as follows to
   * gracefully deals with these collisions, and produce a {@code Map} mapping names to a
   * concatenated list of addresses:
   * <pre>{@code
   *     Map<String, String> phoneBook
   *         people.stream().collect(toMap(Person::getName,
   *                                       Person::getAddress,
   *                                       (s, a) -> s + ", " + a));
   * }</pre>
   * @implNote The returned {@code Collector} is not concurrent.  For parallel stream pipelines, the
   * {@code combiner} function operates by merging the keys from one map into another, which can be
   * an expensive operation.  If it is not required that results are merged into the {@code Map} in
   * encounter order, using {@link #toConcurrentMap(Function, Function, BinaryOperator)} may offer
   * better parallel performance.
   * @see #toMap(Function, Function)
   * @see #toMap(Function, Function, BinaryOperator, Supplier)
   * @see #toConcurrentMap(Function, Function, BinaryOperator)
   */
  public static <T, K, U>
  Collector<T, ?, Map<K, U>> toMap(Function<? super T, ? extends K> keyMapper,
      Function<? super T, ? extends U> valueMapper,
      BinaryOperator<U> mergeFunction) {
    return toMap(keyMapper, valueMapper, mergeFunction, HashMap::new);
  }

  /**
   * Returns a {@code Collector} that accumulates elements into a
   * {@code Map} whose keys and values are the result of applying the provided
   * mapping functions to the input elements.
   *
   * <p>If the mapped
   * keys contains duplicates (according to {@link Object#equals(Object)}),
   * the value mapping function is applied to each equal element, and the
   * results are merged using the provided merging function.  The {@code Map}
   * is created by a provided supplier function.
   *
   * @param <T> the type of the input elements
   * @param <K> the output type of the key mapping function
   * @param <U> the output type of the value mapping function
   * @param <M> the type of the resulting {@code Map}
   * @param keyMapper a mapping function to produce keys
   * @param valueMapper a mapping function to produce values
   * @param mergeFunction a merge function, used to resolve collisions between values associated
   * with the same key, as supplied to {@link Map#merge(Object, Object, BiFunction)}
   * @param mapSupplier a function which returns a new, empty {@code Map} into which the results
   * will be inserted
   * @return a {@code Collector} which collects elements into a {@code Map} whose keys are the
   * result of applying a key mapping function to the input elements, and whose values are the
   * result of applying a value mapping function to all input elements equal to the key and
   * combining them using the merge function
   * @implNote The returned {@code Collector} is not concurrent.  For parallel stream pipelines, the
   * {@code combiner} function operates by merging the keys from one map into another, which can be
   * an expensive operation.  If it is not required that results are merged into the {@code Map} in
   * encounter order, using {@link #toConcurrentMap(Function, Function, BinaryOperator, Supplier)}
   * may offer better parallel performance.
   * @see #toMap(Function, Function)
   * @see #toMap(Function, Function, BinaryOperator)
   * @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
   */
  public static <T, K, U, M extends Map<K, U>>
  Collector<T, ?, M> toMap(Function<? super T, ? extends K> keyMapper,
      Function<? super T, ? extends U> valueMapper,
      BinaryOperator<U> mergeFunction,
      Supplier<M> mapSupplier) {
    BiConsumer<M, T> accumulator
        = (map, element) -> map.merge(keyMapper.apply(element),
        valueMapper.apply(element), mergeFunction);
    return new CollectorImpl<>(mapSupplier, accumulator, mapMerger(mergeFunction), CH_ID);
  }

  /**
   * Returns a concurrent {@code Collector} that accumulates elements into a
   * {@code ConcurrentMap} whose keys and values are the result of applying
   * the provided mapping functions to the input elements.
   *
   * <p>If the mapped keys contains duplicates (according to
   * {@link Object#equals(Object)}), an {@code IllegalStateException} is
   * thrown when the collection operation is performed.  If the mapped keys
   * may have duplicates, use
   * {@link #toConcurrentMap(Function, Function, BinaryOperator)} instead.
   *
   * @param <T> the type of the input elements
   * @param <K> the output type of the key mapping function
   * @param <U> the output type of the value mapping function
   * @param keyMapper the mapping function to produce keys
   * @param valueMapper the mapping function to produce values
   * @return a concurrent, unordered {@code Collector} which collects elements into a {@code
   * ConcurrentMap} whose keys are the result of applying a key mapping function to the input
   * elements, and whose values are the result of applying a value mapping function to the input
   * elements
   * @apiNote It is common for either the key or the value to be the input elements. In this case,
   * the utility method {@link java.util.function.Function#identity()} may be helpful. For example,
   * the following produces a {@code Map} mapping students to their grade point average:
   * <pre>{@code
   *     Map<Student, Double> studentToGPA
   *         students.stream().collect(toMap(Functions.identity(),
   *                                         student -> computeGPA(student)));
   * }</pre>
   * And the following produces a {@code Map} mapping a unique identifier to students:
   * <pre>{@code
   *     Map<String, Student> studentIdToStudent
   *         students.stream().collect(toConcurrentMap(Student::getId,
   *                                                   Functions.identity());
   * }</pre>
   *
   * <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and {@link
   * Collector.Characteristics#UNORDERED unordered} Collector.
   * @see #toMap(Function, Function)
   * @see #toConcurrentMap(Function, Function, BinaryOperator)
   * @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
   */
  public static <T, K, U>
  Collector<T, ?, ConcurrentMap<K, U>> toConcurrentMap(Function<? super T, ? extends K> keyMapper,
      Function<? super T, ? extends U> valueMapper) {
    return toConcurrentMap(keyMapper, valueMapper, throwingMerger(), ConcurrentHashMap::new);
  }

  /**
   * Returns a concurrent {@code Collector} that accumulates elements into a
   * {@code ConcurrentMap} whose keys and values are the result of applying
   * the provided mapping functions to the input elements.
   *
   * <p>If the mapped keys contains duplicates (according to {@link Object#equals(Object)}),
   * the value mapping function is applied to each equal element, and the
   * results are merged using the provided merging function.
   *
   * @param <T> the type of the input elements
   * @param <K> the output type of the key mapping function
   * @param <U> the output type of the value mapping function
   * @param keyMapper a mapping function to produce keys
   * @param valueMapper a mapping function to produce values
   * @param mergeFunction a merge function, used to resolve collisions between values associated
   * with the same key, as supplied to {@link Map#merge(Object, Object, BiFunction)}
   * @return a concurrent, unordered {@code Collector} which collects elements into a {@code
   * ConcurrentMap} whose keys are the result of applying a key mapping function to the input
   * elements, and whose values are the result of applying a value mapping function to all input
   * elements equal to the key and combining them using the merge function
   * @apiNote There are multiple ways to deal with collisions between multiple elements mapping to
   * the same key.  The other forms of {@code toConcurrentMap} simply use a merge function that
   * throws unconditionally, but you can easily write more flexible merge policies.  For example, if
   * you have a stream of {@code Person}, and you want to produce a "phone book" mapping name to
   * address, but it is possible that two persons have the same name, you can do as follows to
   * gracefully deals with these collisions, and produce a {@code Map} mapping names to a
   * concatenated list of addresses:
   * <pre>{@code
   *     Map<String, String> phoneBook
   *         people.stream().collect(toConcurrentMap(Person::getName,
   *                                                 Person::getAddress,
   *                                                 (s, a) -> s + ", " + a));
   * }</pre>
   *
   * <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and {@link
   * Collector.Characteristics#UNORDERED unordered} Collector.
   * @see #toConcurrentMap(Function, Function)
   * @see #toConcurrentMap(Function, Function, BinaryOperator, Supplier)
   * @see #toMap(Function, Function, BinaryOperator)
   */
  public static <T, K, U>
  Collector<T, ?, ConcurrentMap<K, U>>
  toConcurrentMap(Function<? super T, ? extends K> keyMapper,
      Function<? super T, ? extends U> valueMapper,
      BinaryOperator<U> mergeFunction) {
    return toConcurrentMap(keyMapper, valueMapper, mergeFunction, ConcurrentHashMap::new);
  }

  /**
   * Returns a concurrent {@code Collector} that accumulates elements into a
   * {@code ConcurrentMap} whose keys and values are the result of applying
   * the provided mapping functions to the input elements.
   *
   * <p>If the mapped keys contains duplicates (according to {@link Object#equals(Object)}),
   * the value mapping function is applied to each equal element, and the
   * results are merged using the provided merging function.  The
   * {@code ConcurrentMap} is created by a provided supplier function.
   *
   * <p>This is a {@link Collector.Characteristics#CONCURRENT concurrent} and
   * {@link Collector.Characteristics#UNORDERED unordered} Collector.
   *
   * @param <T> the type of the input elements
   * @param <K> the output type of the key mapping function
   * @param <U> the output type of the value mapping function
   * @param <M> the type of the resulting {@code ConcurrentMap}
   * @param keyMapper a mapping function to produce keys
   * @param valueMapper a mapping function to produce values
   * @param mergeFunction a merge function, used to resolve collisions between values associated
   * with the same key, as supplied to {@link Map#merge(Object, Object, BiFunction)}
   * @param mapSupplier a function which returns a new, empty {@code Map} into which the results
   * will be inserted
   * @return a concurrent, unordered {@code Collector} which collects elements into a {@code
   * ConcurrentMap} whose keys are the result of applying a key mapping function to the input
   * elements, and whose values are the result of applying a value mapping function to all input
   * elements equal to the key and combining them using the merge function
   * @see #toConcurrentMap(Function, Function)
   * @see #toConcurrentMap(Function, Function, BinaryOperator)
   * @see #toMap(Function, Function, BinaryOperator, Supplier)
   */
  public static <T, K, U, M extends ConcurrentMap<K, U>>
  Collector<T, ?, M> toConcurrentMap(Function<? super T, ? extends K> keyMapper,
      Function<? super T, ? extends U> valueMapper,
      BinaryOperator<U> mergeFunction,
      Supplier<M> mapSupplier) {
    BiConsumer<M, T> accumulator
        = (map, element) -> map.merge(keyMapper.apply(element),
        valueMapper.apply(element), mergeFunction);
    return new CollectorImpl<>(mapSupplier, accumulator, mapMerger(mergeFunction),
        CH_CONCURRENT_ID);
  }

  /**
   * Returns a {@code Collector} which applies an {@code int}-producing
   * mapping function to each input element, and returns summary statistics
   * for the resulting values.
   *
   * @param <T> the type of the input elements
   * @param mapper a mapping function to apply to each element
   * @return a {@code Collector} implementing the summary-statistics reduction
   * @see #summarizingDouble(ToDoubleFunction)
   * @see #summarizingLong(ToLongFunction)
   */
  public static <T>
  Collector<T, ?, IntSummaryStatistics> summarizingInt(ToIntFunction<? super T> mapper) {
    return new CollectorImpl<T, IntSummaryStatistics, IntSummaryStatistics>(
        IntSummaryStatistics::new,
        (r, t) -> r.accept(mapper.applyAsInt(t)),
        (l, r) -> {
          l.combine(r);
          return l;
        }, CH_ID);
  }

  /**
   * Returns a {@code Collector} which applies an {@code long}-producing
   * mapping function to each input element, and returns summary statistics
   * for the resulting values.
   *
   * @param <T> the type of the input elements
   * @param mapper the mapping function to apply to each element
   * @return a {@code Collector} implementing the summary-statistics reduction
   * @see #summarizingDouble(ToDoubleFunction)
   * @see #summarizingInt(ToIntFunction)
   */
  public static <T>
  Collector<T, ?, LongSummaryStatistics> summarizingLong(ToLongFunction<? super T> mapper) {
    return new CollectorImpl<T, LongSummaryStatistics, LongSummaryStatistics>(
        LongSummaryStatistics::new,
        (r, t) -> r.accept(mapper.applyAsLong(t)),
        (l, r) -> {
          l.combine(r);
          return l;
        }, CH_ID);
  }

  /**
   * Returns a {@code Collector} which applies an {@code double}-producing
   * mapping function to each input element, and returns summary statistics
   * for the resulting values.
   *
   * @param <T> the type of the input elements
   * @param mapper a mapping function to apply to each element
   * @return a {@code Collector} implementing the summary-statistics reduction
   * @see #summarizingLong(ToLongFunction)
   * @see #summarizingInt(ToIntFunction)
   */
  public static <T>
  Collector<T, ?, DoubleSummaryStatistics> summarizingDouble(ToDoubleFunction<? super T> mapper) {
    return new CollectorImpl<T, DoubleSummaryStatistics, DoubleSummaryStatistics>(
        DoubleSummaryStatistics::new,
        (r, t) -> r.accept(mapper.applyAsDouble(t)),
        (l, r) -> {
          l.combine(r);
          return l;
        }, CH_ID);
  }

  /**
   * Implementation class used by partitioningBy.
   */
  private static final class Partition<T>
      extends AbstractMap<Boolean, T>
      implements Map<Boolean, T> {

    final T forTrue;
    final T forFalse;

    Partition(T forTrue, T forFalse) {
      this.forTrue = forTrue;
      this.forFalse = forFalse;
    }

    @Override
    public Set<Map.Entry<Boolean, T>> entrySet() {
      return new AbstractSet<Map.Entry<Boolean, T>>() {
        @Override
        public Iterator<Map.Entry<Boolean, T>> iterator() {
          Map.Entry<Boolean, T> falseEntry = new SimpleImmutableEntry<>(false, forFalse);
          Map.Entry<Boolean, T> trueEntry = new SimpleImmutableEntry<>(true, forTrue);
          return Arrays.asList(falseEntry, trueEntry).iterator();
        }

        @Override
        public int size() {
          return 2;
        }
      };
    }
  }
}
